ArticleSensors (Basel, Switzerland)2022
2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- An Improved Unfolded Coprime Linear Array Design for DOA Estimation with No Phase Ambiguity.Sensors (Basel, Switzerland) · 2024Article
- Coherent DOA Estimation Algorithm with Co-Prime Arrays for Low SNR Signals.Sensors (Basel, Switzerland) · 2023Article
- An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation.Sensors (Basel, Switzerland) · 2023Article
- Signal Processing and Machine Learning for Smart Sensing Applications.Sensors (Basel, Switzerland) · 2023Article
- Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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Abstract
In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm for 2D-DOA estimation in a SUCA. The proposed algorithm estimates the complete covariance matrix of a fully sampled UCA (FUCA) from the sample covariance matrix of the SUCA through a neural network. Afterwards, the MUSIC algorithm is performed for 2D-DOA estimation with the completed covariance matrix. We conduct Monte Carlo simulations to evaluate the performance of the proposed algorithm in various scenarios; the performance of 2D-DOA estimation in the SUCA gradually approaches that in the FUCA as the SNR or the number of snapshots increases, which means that the advantages of a FUCA can be preserved with fewer RF chains. In addition, the proposed algorithm is able to implement underdetermined 2D-DOA estimation.
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